Multimedia prefetching with optimal Markovian policies

Cezar Pleşca (1) , Vincent Charvillat (2) , Wei Tsang Ooi (3)

1 MTA - ATM - Military Technical Academy - Académie Technique Militaire [Bucharest, Romania]
2 IRIT-REVA - Real Expression Artificial Life
3 NUS - National University of Singapore

Multimedia prefetching is able to reduce the end-to-end latency and improve the video quality perceived by users. Previous work modeled prefetching as applying sequential decisional policies under uncertainty, using a Markov Decision Process model that integrated both user behavior and resources availability, to achieve optimal prefetching policies under realistic assumptions. In this paper, we extended the existing MDP model by considering more complex and aggressive policies, while preserving the optimality of the prefetching policy. We further enriched the extended model by considering user's profile to provide finer prefetching policies. The proposed extensions and the associated policies are validated through comparison against the existing model and against heuristics found in related work. We showed that our profiles-aware optimal policies can be achieved up to 28% latency reduction with respect to known heuristics.

Mots clés: Multimedia; Prefetching; Markov decision process; Profiling; Uncertainty; Optimization

Domaines: Intelligence artificielle [cs.AI]

Cezar Pleşca, Vincent Charvillat, Wei Tsang Ooi. Multimedia prefetching with optimal Markovian policies. Journal of Network and Computer Applications (JNCA), 2016, 69, pp.40-53. ⟨10.1016/j.jnca.2016.05.002⟩. ⟨hal-03463782⟩